nips nips2002 nips2002-74 nips2002-74-reference knowledge-graph by maker-knowledge-mining

74 nips-2002-Dynamic Structure Super-Resolution


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Author: Amos J. Storkey

Abstract: The problem of super-resolution involves generating feasible higher resolution images, which are pleasing to the eye and realistic, from a given low resolution image. This might be attempted by using simple filters for smoothing out the high resolution blocks or through applications where substantial prior information is used to imply the textures and shapes which will occur in the images. In this paper we describe an approach which lies between the two extremes. It is a generic unsupervised method which is usable in all domains, but goes beyond simple smoothing methods in what it achieves. We use a dynamic tree-like architecture to model the high resolution data. Approximate conditioning on the low resolution image is achieved through a mean field approach. 1


reference text

[1] N. J. Adams. Dynamic Trees: A Hierarchical Probabilistic Approach to Image Modelling. PhD thesis, Division of Informatics, University of Edinburgh, 5 Forrest Hill, Edinburgh, EH1 2QL, UK, 2001.

[2] S. Baker and T. Kanade. Limits on super-resolution and how to break them. In Proceedings of CVPR 00, pages 372–379, 2000.

[3] P. Cheeseman, B. Kanefsky, R. Kraft, and J. Stutz. Super-resolved surface reconstruction from multiple images. Technical Report FIA-94-12, NASA Ames, 1994.

[4] M. Elad and A. Feuer. Super-resolution reconstruction of image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(9):817–834, 1999.

[5] W. T. Freeman, T. R. Jones, and E. C. Pasztor. Markov networks for super-resolution. Technical Report TR-2000-08, MERL, 2000.

[6] W. T. Freeman, T. R. Jones, and E. C. Pasztor. Example-based super-resolution. IEEE Computer Graphics and Applications, 2002.

[7] R. R. Schultz and R. L. Stevenson. A Bayesian approach to image expansion for improved definition. IEEE Transactions on Image Processing, 3:233–242, 1994.

[8] A. J. Storkey. Dynamic trees: A structured variational method giving efficient propagation rules. In C. Boutilier and M. Goldszmidt, editors, Uncertainty in Artificial Intelligence, pages 566–573. Morgan Kauffmann, 2000.

[9] C. K. I. Williams and N. J. Adams. DTs: Dynamic trees. In M. J. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11. MIT Press, 1999.